One thing most engineers have in common is a fascination for elliptic gears. And with the impending release of STAR-CCM+ v8.04, it will be easier than ever to set the wheels in motion.

Indeed, one of the most exciting features of STAR-CCM+ v8.04 is an enhanced overset mesh technology which allows for multiple overlapping overset zones. Overset meshes, also known as “Chimera” or overlapping grids, may be used to simulate the relative motion of one or more bodies, including arbitrary or tangential motions of objects in close proximity. And whereas it has been possible to overlap overset grids with a background mesh since the release of STAR-CCM+ v7.02, it will now also be possible to overlap overset grids with each other.

The biggest challenge of gradient-based shape optimization using high-fidelity CFD has always been the formidable computational expense tied to constructing the sensitivity of the objective (or cost) functions with respect to the design variables.

When it comes to systems, one of the most complex (and perhaps least understood) of all is that of the human body. The average adult human body is, on average, 57% - 60% water. That's a lot of fluid! So it stands to reason that CFD is a great tool for simulating the systems of things like medical implants, surgical techniques, diagnostic systems and the like.

A recent artilce published in Desktop Engineering examines how CFD is making an impact in the medical field. Our own Krisitan Debus Ph.D. talks about our work with an ASME sub-committee, writing verification and validation guidelines for biomedical devices as well as STAR-CCM+'s very useful overset mesh feature. Read the entire article here.

One of the best illustrations of "Simulating Systems" is this video from the STAR Global Conference 2013, in which Scott D. Reynolds of M/E Engineering explores the use of CFD for studying the impact of wind on the built environment.

Asked to examine the influence of helicopter exhaust plumes on surrounding bulidings, M/E Engineering decided to simulate the whole system, including fully unsteady wind profiles (with gusts that vary in speed and direction), and the full complexity of the local urban landscape. Best of all, the simulation includes an actual moving helicopter that entrains gas from nearby building plumes as it takes off and lands.

"Prediction is very difficult, especially if it's about the future" - Niels Bohr, Nobel laureate in Physics

Whether you like it or not, as a simulation engineer you are in the prediction game. Put simply, your job is to predict how an abstract design would perform in the real world, hopefully accounting for the most challenging operating conditions that it would likely experience during its working life.Compared with other professional forecasters such as economists, television meteorologists or political commentators, the audience for engineering predictions is more critical and less likely to forgive. While incorrect weather forecasts are quickly forgotten (at least those that don't involve hurricanes), and one rarely takes economists seriously, the cost of getting an engineering prediction wrong can be enormous. The failure of a product in service can have serious consequences, particularly in the case of safety critical applications where unforeseen failure can result in injury or loss-of-life. Even in less serious circumstances, the unexpected failure of a product can act to de-motivate consumers, damaging brand reputation, potentially incurring large warranty expenses.

The problem is that uncertainty is a fundamental part of all prediction; no engineering prediction is perfect and no simulation model is a complete representation of the real world scenario. Every model is based upon a set of underlying assumptions that allows it to be solved numerically, but ultimately influences the accuracy of the prediction. As engineers, we are responsible for acknowledging and understanding the uncertainty in our predictions and, wherever possible, to try and minimize that uncertainty through the application of judicious modeling assumptions.

As a follow up to yesterday's post, here's a presentation from the 2013 STAR Global Conference which took place in Orlando, FL this past March. Michael Carl of Rowan Williams Davis & Irwin, Inc., shows how STAR-CCM+ was used to evaluate the egress system on a bus deck during a fire. The system was simulated as a whole as well as sectionally, including wind, fire and water from the sprinklers.